Biogeosciences, 12, 2207–2226, 2015

www.biogeosciences.net/12/2207/2015/

doi:10.5194/bg-12-2207-2015

© Author(s) 2015. CC Attribution 3.0 License.

Global analysis of seasonality in the shell flux of extant

planktonic Foraminifera

L. Jonkers1 and M. Kucera2

1School of Earth and Ocean Sciences, Cardiff University, Main building, Park Place, Cardiff, CF10 3AT, Wales, UK2MARUM & Fachbereich Geowissenschaften, Universität Bremen, Leobener Strasse, 28359 Bremen, Germany

Correspondence to: L. Jonkers (jonkersl@cardiff.ac.uk)

Received: 5 November 2014 – Published in Biogeosciences Discuss.: 21 January 2015

Revised: 19 March 2015 – Accepted: 22 March 2015 – Published: 15 April 2015

Abstract. Shell fluxes of planktonic Foraminifera species

vary intra-annually in a pattern that appears to follow the

seasonal cycle. However, the variation in the timing and

prominence of seasonal flux maxima in space and among

species remains poorly constrained. Thus, although chang-

ing seasonality may result in a flux-weighted temperature

offset of more than 5◦ C within a species, this effect is of-

ten ignored in the interpretation of Foraminifera-based pale-

oceanographic records. To address this issue we present an

analysis of the intra-annual pattern of shell flux variability in

37 globally distributed time series. The existence of a sea-

sonal component in flux variability was objectively charac-

terised using periodic regression. This analysis yielded es-

timates of the number, timing and prominence of seasonal

flux maxima. Over 80 % of the flux series across all species

showed a statistically significant periodic component, indi-

cating that a considerable part of the intra-annual flux vari-

ability is predictable. Temperature appears to be a power-

ful predictor of flux seasonality, but its effect differs among

species. Three different modes of seasonality are distinguish-

able. Tropical and subtropical species (Globigerinoides ru-

ber (white and pink varieties), Neogloboquadrina dutertrei,

Globigerinoides sacculifer, Orbulina universa, Globiger-

inella siphonifera, Pulleniatina obliquiloculata, Globoro-

talia menardii, Globoturborotalita rubescens, Globoturboro-

talita tenella and Globigerinoides conglobatus) appear to

have a less predictable flux pattern, with random peak tim-

ing in warm waters. In colder waters, seasonality is more

prevalent: peak fluxes occur shortly after summer temper-

ature maxima and peak prominence increases. This ten-

dency is stronger in species with a narrower temperature

range, implying that warm-adapted species find it increas-

ingly difficult to reproduce outside their optimum tempera-

ture range and that, with decreasing mean temperature, their

flux is progressively more focussed in the warm season. The

second group includes the temperate to cold-water species

Globigerina bulloides, Globigerinita glutinata, Turborotalita

quinqueloba, Neogloboquadrina incompta, Neogloboquad-

rina pachyderma, Globorotalia scitula, Globigerinella cal-

ida, Globigerina falconensis, Globorotalia theyeri and Glo-

bigerinita uvula. These species show a highly predictable

seasonal pattern, with one to two peaks a year, which occur

earlier in warmer waters. Peak prominence in this group is in-

dependent of temperature. The earlier-when-warmer pattern

in this group is related to the timing of productivity maxima.

Finally, the deep-dwelling Globorotalia truncatulinoides and

Globorotalia inflata show a regular and pronounced peak

in winter and spring. The remarkably low flux outside the

main pulse may indicate a long reproductive cycle of these

species. Overall, our analysis indicates that the seasonality

of planktonic Foraminifera shell flux is predictable and re-

veals the existence of distinct modes of phenology among

species. We evaluate the effect of changing seasonality on pa-

leoceanographic reconstructions and find that, irrespective of

the seasonality mode, the actual magnitude of environmental

change will be underestimated. The observed constraints on

flux seasonality can serve as the basis for predictive mod-

elling of flux pattern. As long as the diversity of species sea-

sonality is accounted for in such models, the results can be

used to improve reconstructions of the magnitude of environ-

mental change in paleoceanographic records.

Published by Copernicus Publications on behalf of the European Geosciences Union.

2208 L. Jonkers and M. Kucera: Global analysis of seasonality in the shell flux

1 Introduction

Planktonic Foraminifera are unicellular marine zooplankton

with a global distribution. About 40 extant morphospecies

are known and a number of these are symbiont-bearing

(Hemleben et al., 1989). Planktonic Foraminifera build a cal-

cite shell, which rapidly sinks after the death of the organ-

ism (Takahashi and Bé, 1984). Above the carbonate com-

pensation depth, these shells are well preserved and may

form an important part of the sediment. As a result, plank-

tonic Foraminifera play a significant role in the marine car-

bonate cycle (Schiebel, 2002) and their fossil record is an

important source of information on the physical and chemi-

cal conditions of past oceans. However, the interpretation of

Foraminifera-based proxies of past environmental change is

not straightforward and requires detailed knowledge on the

ecology of the species involved.

The abundance of planktonic Foraminifera and the export

flux of their shells vary both spatially and temporally (e.g.

Bé, 1960; Bé and Tolderlund, 1971; Deuser et al., 1981).

Timescales of temporal variability range from less than 1

month (lunar) to seasonal and interannual scales and beyond

(e.g. Deuser et al., 1981; Marchant et al., 2004; Spindler et

al., 1979). The intra-annual variability can range over several

orders of magnitude (Deuser et al., 1981; Tolderlund and Bé,

1971) and because it resonates with the seasonal cycle of en-

vironmental conditions, it has a particularly large potential to

modify the proxy signal recorded in a fossil assemblage. For

example, apart from in the tropics, the seasonal sea surface

temperature variability is always greater than the interannual

variability (Mackas et al., 2012). Hence, a thorough under-

standing of the seasonal cycle in planktonic Foraminifera

is essential to improve our ability to reconstruct past ocean

changes.

Like in other groups of marine plankton, seasonality in

planktonic Foraminifera can in principle manifest itself in

two ways: (i) through changes in the timing of the peak flux

or peak abundance and (ii) through changes in the amplitude

of the seasonal cycle. Both aspects of seasonality could affect

planktonic Foraminifera proxies, leading to seasonal biases

in the variables recorded by fossil assemblages. In the case

of temperature reconstructions, this can lead to offsets from

mean conditions by several degrees (e.g. Fraile et al., 2009a;

Jonkers et al., 2010). Differences in the seasonal pattern of

shell flux among species have been exploited to reconstruct

variations in seasonal temperatures (Saher et al., 2007). In

addition, seasonality is thought to explain part of the differ-

ence between proxies that are Foraminifera-based and other

proxies (Laepple and Huybers, 2013; Leduc et al., 2010).

Besides being species-specific (Deuser et al., 1981), sea-

sonality in planktonic Foraminifera fluxes also varies spa-

tially within individual species (Tolderlund and Bé, 1971),

implying that the seasonal cycle is under environmental con-

trol. Consequently, seasonality may have changed as a func-

tion of climate and/or oceanic change through time. Until

now, few studies have investigated the effect of such tran-

sient changes in seasonality on foraminiferal records. Using

a Foraminifera model coupled to global simulations with an

ecosystem model, Fraile et al. (2009b) have shown that the

timing of the maximum production of Foraminifera species

could have shifted by as much as 6 months between the last

glacial maximum and the present day, but explicit efforts to

quantify seasonal bias in planktonic foraminiferal proxies re-

main challenging (Schneider et al., 2010).

In most sediment trap studies, the recurrence time of

the flux peaks is linked to the seasonal pattern of tem-

perature (Zaric et al., 2005). Clearly, as with other zoo-

plankton (Mackas et al., 2012; Richardson, 2008), temper-

ature appears to be an important factor controlling the phe-

nology of planktonic Foraminifera. However, several other

parameters, some(times) correlated with temperature, such

as food availability, nutrient availability, predation, com-

petition, light availability and salinity have also been sug-

gested to be important controls, particularly within the opti-

mum temperature range of a species (Hemleben et al., 1989;

Northcote and Neil, 2005; Ortiz and Mix, 1992; Ortiz et al.,

1995). Until now there has been no effort to assess flux sea-

sonality in sediment trap records on a global scale. Instead of

an evaluation of observational data, recent investigations of

seasonality in planktonic Foraminifera were based on models

(Fraile et al., 2008; Lombard et al., 2011; Zaric et al., 2006).

Despite the different approaches, these models provided a

reasonable first-order description of the seasonality in plank-

tonic Foraminifera. However, our incomplete understanding

of the mechanisms that control seasonality hampers further

improvements and validation of these models and prevents

accounting for seasonality effects on paleorecords. This is in

part due to the lack of a global overview of foraminiferal sea-

sonality.

Here we present such a synthesis based on a large number

of sediment trap time series from across the world oceans.

Sediment traps provide continuous time series of settling

shell fluxes, typically at a resolution of less than 1 month,

and are therefore ideal for studying phenology. Importantly,

shell fluxes represent the settling of dead Foraminifera and

are strictly speaking not directly related to the abundance of

Foraminifera in the water column. However, only a limited

number of repeated plankton net surveys, which are needed

to assess seasonal water column abundance, exist (Field,

2004; Schiebel et al., 1997), and these are often of too low

a resolution and duration to assess seasonality. We therefore

assess phenology from the settling fluxes, which are, given

the short life span of Foraminifera (Hemleben et al., 1989),

likely to be strongly linked to abundance. Our aim is to de-

termine the extent of the seasonal component in the flux data

and evaluate its predictability.

Biogeosciences, 12, 2207–2226, 2015 www.biogeosciences.net/12/2207/2015/

L. Jonkers and M. Kucera: Global analysis of seasonality in the shell flux 2209

−150 −100 −50 0 50 100 150

−50

050

●

●●● ●●● ● ●●● ● ●●●● ●●●

● ●●●●●● ●

● ●●●●

●

●●

● ●

1234

567 8 91011 12

13141516 17181920 2122 23 24

2526 27

28 2930 313233

3435

36 37

● ● ● ● ● ●1−2 2−3 3−4 4−5 5−7 7−10duration: years

Figure 1. Distribution and duration of sediment trap time series used in this study. Site numbers refer to Table 1.

2 Data and methods

To analyse the seasonality of planktonic Foraminifera shell

flux, we produced a compilation of globally distributed

moored sediment trap time series with a duration of at least 1

year. Traps close to the sea floor or in the vicinity of (steep)

topography that showed the influence of resuspended mate-

rial (for instance, the presence of benthic Foraminifera) were

not considered. In cases were multiple sediment traps were

deployed at different depths on the same mooring, we re-

port data from the shallowest trap as this (likely) reduces the

catchment area of the trap and the data hence reflect local

conditions more closely. The complete data set contains 37

time series (Table 1). The sites are unevenly distributed glob-

ally, with the majority (28) from the Northern Hemisphere

and an important cluster of 8 traps in the northwest Pacific

(Fig. 1). Nevertheless, the data span a temperature range of

almost 30◦ C without large gaps. Trap depths varied between

250 and 5000 m (Table 1), and several sites are close to the

continents, but only a single sediment trap was deployed at a

location with a water depth of less than 1 km. We therefore

consider the sites to reflect pelagic settings. The average du-

ration of the time series is 2.75 years, and the longer ones are

predominantly from the Northern Hemisphere (Fig. 1). The

mean length of the collection interval was 19 days but varied

between 4 days and 1 month (Fig. 2a). Collection intervals

were often also variable during the deployments. Most stud-

ies reported fluxes of shells of more than 125 or 150 µm; four

used narrower size ranges (Table 1). We report here on 23

species for which counts in five or more time series are avail-

able (Table 2). The taxonomy in all time series has been har-

monised such that the Neogloboquadrina pachyderma (right-

coiling) is considered as a separate species and referred to as

Neogloboquadrina incompta, Globigerinella aequilateralis

is referred to as Globigerinella siphonifera, and Globigeri-

noides trilobus and G. quadrilobatus are included in Glo-

bigerinoides sacculifer. In addition Globigerina umbilicata

is considered synonymous with G. bulloides. Since very few

studies reported coiling direction in Globorotalia truncat-

ulinoides, no distinction was made between left- and right-

coiling varieties of this species. Additional remarks specific

to individual time series are included in Table 1.

To link the observed shell flux patterns to environmental

conditions, temperature and productivity data were extracted

for the positions of all traps. Temperature has been consid-

ered because it is known to play a key role in the phenol-

ogy of other zooplankton (Mackas et al., 2012; Richardson,

2008). Most of the other parameters suggested as influenc-

ing the phenology of planktonic Foraminifera, such as light,

mixed layer depth and nutrient availability, only affect their

seasonality indirectly by affecting the phytoplankton, a major

food source for many planktonic foraminiferal species. This

results in a predictable phase relationship, with zooplankton

always lagging behind phytoplankton. Thus, the inclusion of

temperature and productivity should allow us to study both

end-member scenarios, with Foraminifera responding ex-

clusively to temperature or following productivity. Because

continuous observations of near surface conditions from the

moorings are not available, temperature data for the upper

water column (0–50 m) were taken from the 2009 World

Ocean Atlas (Locarnini et al., 2010). Using climatology data

also allows us to use upper-ocean temperatures that are more

representative of Foraminifera habitat than surface temper-

atures derived from remote sensing. Moreover, interannual

temperature variability at individual sites is typically small

(often less than 1◦ C) compared to the temperature differ-

ences between sites (∼ 30◦ C), warranting the use of clima-

tology data since our objective is to compare the patterns

among sites. We use sea surface chlorophyll a concentration

data as an indicator of upper-ocean productivity. Since a large

part of the shell flux time series predates the era of satellite

www.biogeosciences.net/12/2207/2015/ Biogeosciences, 12, 2207–2226, 2015

2210 L. Jonkers and M. Kucera: Global analysis of seasonality in the shell flux

Table 1. Details of shell flux time series used in this study. Site numbers refer to Fig. 1.

Site no. Lat (◦ N) Long (◦ E) Deployment depth (m) Duration (days) Size (µm) Remarks References

1 69.7 −0.5 500 780 63–500 Jensen, 1998

2 59.3 149.8 258 365 > 150 N. dutertrei removed Alderman, 1996

3 59.0 −38.5 2750 988 150–315 T. quinqueloba Jonkers et al., 2010,

150–250 µm 2013

4 53.1 −177.0 3198 2926 > 125 Asahi and Takahashi, 2007

5 50.0 165.0 3260 1141 > 125 Kuroyanagi et al., 2002

6 50.0 −145.0 3800 1128 > 125 Sautter and Thunell, 1989

7 49.0 −174.0 4812 2803 > 125 Asahi and Takahashi, 2007

8 48.0 −21.0 2000–3700 378 > 150 Depth change Wolfteich, 1994

9 44.0 155.0 2957 851 > 125 Kuroyanagi et al., 2002

10 43.0 5.2 500 3338 > 150 Rigual-Hernández et al., 2012

11 42.4 3.5 500 3552 > 150 Rigual-Hernández et al., 2012

12 42.0 155.2 1091 380 > 125 Mohiuddin et al., 2005

13 40.0 165.0 2986 768 > 125 Kuroyanagi et al., 2002

14 39.0 147.0 1371–1586 608 > 125 Depth change Mohiuddin et al., 2002

15 36.7 154.9 5034 376 > 125 Mohiuddin et al., 2004

16 34.3 −120.0 590; 470 1108 > 125 Depth change Darling et al., 2003;

Kincaid et al., 2000

17 34.0 −21.0 2000 378 > 150 Wolfteich, 1994

18 33.0 22.0 3000 764 > 125 Storz et al., 2009

19 32.1 −64.3 3200 1848 > 125 Deuser and Ross, 1989;

Deuser et al., 1981

20 27.5 −90.3 700 1563 > 150 Poore et al., 2013;

Reynolds et al., 2013

21 25.0 137.0 917–1388 573 > 125 Depth change Mohiuddin et al., 2002

22 21.2 −20.7 3500 718 > 150 Fischer et al., 1996;

Zaric et al., 2005

23 16.3 60.5 3020 508 > 125 Curry et al., 1992

24 15.5 68.8 2790 482 > 125 Curry et al., 1992

25 14.5 64.8 732 486 > 125 Curry et al., 1992

26 10.5 −65.5 Not reported 1002 > 125 Tedesco and Thunell, 2003

27 9.4 113.2 720 662 > 154 Wan et al., 2010

28 −7.5 −28.0 671 500 > 150 Zaric et al., 2005

29 −8.3 108.0 1370–1860 963 > 150 Depth change Mohtadi et al., 2009

30 −11.6 −28.5 710 944 > 150 Zaric et al., 2005

31 −16.8 40.8 2250 863 250–315 Fallet et al., 2010, 2011

32 −20.0 9.2 1648 524 > 150 Zaric et al., 2005

33 −30.0 −73.0 2300–2500 1563 > 150 Marchant et al., 2004, 1998

34 −46.8 142.0 3800 464 > 150 King and Howard, 2003

35 −52.6 174.2 442; 362 410 > 150 Depth change Northcote and Neil, 2005

36 −62.5 −34.8 863 418 > 125 Donner and Wefer, 1994

37 −64.9 −2.5 360 745 > 125 Donner and Wefer, 1994

-75-45-1515 45 75

residual [days]

0

40

80

120

160

n

0 100 200 300 400observed peak [DOY]

0

100

200

300

400

mod

elle

d pe

ak [

DO

Y] B C

5 10 15 20 25 30collecting interval [days]

0

4

8

12

16

n

A

Figure 2. Histogram of average collecting interval for all time series (a). Observed vs. modelled peak time (b) and residual of

observed−modelled peak time (c). See Figs. S3 and S4 for individual species. Solid symbols in (b) and grey shading in (c) denote sin-

gle or first cycles and open symbols second cycles in panel (c).

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L. Jonkers and M. Kucera: Global analysis of seasonality in the shell flux 2211

observations, we use SeaWiFS (Sea-viewing Wide Field-of-

view Sensor) monthly climatology (data downloaded from

http://oceancolor.gsfc.nasa.gov/). This means that interan-

nual productivity variability is ignored, but, similarly to tem-

perature, the interannual range in peak productivity timing

at an individual site (∼ 1 month) is small compared the total

range (12 months) considered here.

To objectively characterise the pattern of the seasonal shell

flux, we have used periodic regression, a simple method that

is suited for cyclic data at low and variable resolutions, ir-

regular starting points and with short duration (Batchelet,

1981; deBruyn and Meeuwig, 2001; Mackas et al., 2012).

In addition to an objective test for the presence of systematic

time-related irregularity in flux, it can detect the number of

peaks (assuming these occur at harmonics of the yearly cy-

cle) and objectively describes the magnitude of the peak flux.

The method has been used extensively to describe cyclic phe-

nomena in biology (Bell et al., 2001; deBruyn and Meeuwig,

2001; Drolet and Barbeau, 2009). Figure 3 illustrates the ap-

proach using an example of Turborotalia quinqueloba from

the Irminger Sea.

Prior to analysis, zero fluxes were replaced with half of

the observed minimum flux and the data were converted to a

log10 scale. All observations were put on the same timescale

by converting the middate of the collection intervals to year

days, which were subsequently angular-transformed using

DOY/365.25× 2π (DOY stands for “day of the year”). No

attempt was made to correct for settling time and/or delay

due to production.

Using linear multiple regression we tested for the presence

of one or two cycles per year in the shell flux by fitting two

different models to the observations:

J (t)= A+B sin(t)+C cos(t) (1)

and

J (t)= A+D sin(2t)+E cos(2t), (2)

where J (t) is the shell flux at time t andA–E are the parame-

ters to be estimated. This approach yields a robust estimate of

the mean – the periodic mean, parameter A in the equations

above – as well as the amplitude and phase angle or peak

timing of the shell flux. The significance of the models was

evaluated using ANOVA for multiple regression. Only model

fits that pass this test with 95 % confidence were considered,

and if both one- and two-cycle models were significant, then

the model that explained more variance (highest adjusted r2)

was chosen. Implicit to this approach are two important as-

sumptions: (i) the seasonal shell flux follows a sinusoidal (i.e.

symmetric) pattern and (ii) in the case of a model with two

cycles per year, there is a 365.25/2 days spacing between the

two peaks. Both assumptions are reasonable given the ecol-

ogy of Foraminifera and the nature of the data, but to confirm

the appropriateness of the method, we have evaluated how

well peak timing is estimated.

To enable a comparison of peak timing from different

hemispheres, dates from sites in the Southern Hemisphere

were transformed to Northern Hemisphere equivalents by

adding or subtracting 182.5 days. For a similar reason, the

comparison of flux amplitude was facilitated by normalising

the log-transformed amplitude of peak flux to the periodic

mean:

PP= log

(10amplitude

10per.mean

). (3)

Expressed in this way, a peak prominence (PP) of 1 describes

a situation where the peak flux is 10 times larger than the

annual mean flux, whereas a value of −1 describes a time

series where the flux during the maximum is only elevated

by 10 % above the annual mean. All calculations were carried

out using R (R core team, 2013), and maps were generated

using package mapplots (Gerritsen, 2012).

3 Results

The 37 globally distributed time series reveal the presence

of large variability in the partitioning of shell flux to seasons

(Fig. S1 in the Supplement). Large variation in seasonality

among species, but importantly also within the same species

at different locations, is evident. Considerable differences ex-

ist between sites close together, but some coherent patterns

appear. Trends towards a peak later in the year or in sum-

mer at higher latitudes are visible in some species (e.g. G.

bulloides and N. incompta). Interestingly, seasonally varying

fluxes do not appear to be exclusive to the extratropics.

3.1 Evaluating periodic regression

The time series in the database have different and often vary-

ing resolutions and durations. In addition, many time series

have gaps due to failing sediment trap rotation or unsuccess-

ful deployments. These factors render an objective charac-

terisation of the seasonal cycle challenging, and we therefore

apply the method of periodic regression. Given the assump-

tions underlying the periodic regression approach, we first

evaluate the appropriateness of the method to describe the

seasonal shell flux cycles. To this end, we test how well peak

timing is estimated using three species from the longest time

series available (Gulf of Lions, site 11, Table 1). This time

series is 10 years long, and the observed interannual variabil-

ity in the peak timing (expressed as the midpoint date of the

sampling interval with highest flux) for the three species is

15 to 38 days. This is comparable to the average difference

between observed peak time and the peak time estimated by

periodic regression for each year separately (21 to 36 days).

The difference between the observed and modelled timing of

peak flux reflects aliasing due to the resolution of the discrete

sampling and the assumption that the flux pattern has a sinu-

soidal shape. The fact that the observed interannual variabil-

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2212 L. Jonkers and M. Kucera: Global analysis of seasonality in the shell flux

Table 2. Results from periodic regression. Species marked with an ∗ have fewer than five observations with significant cyclicity and are

excluded from the analysis.

Species No. in Fig. 6 No. of time series 1 cycle 2 cycle 1 cycle (%) 2 cycle (%) mean PP PP’ (◦ C−1)

Group A

G. ruber (white) 1 20 12 5 60 25 −0.91 −0.09

N. dutertrei 2 17 13 3 76 18 −0.36 −0.03

G. sacculifer 3 18 11 4 61 22 −0.64 −0.07

O. universa 4 18 10 2 56 11 −0.05 −0.06

G. siphonifera 5 17 4 4 24 24 −0.40 −0.06

G. ruber (pink) 6 8 5 2 63 25 −0.07 −0.14

P. obliquiloculata 7 8 3 4 38 50 0.00 −0.48

G. menardii 8 6 2 3 33 50 −0.74 −0.21

G. rubescens 9 6 3 2 50 33 −0.41 −0.17

G. tenella∗ – 7 2 2 29 29 – –

G. conglobatus* – 5 1 1 20 20 – –

Group B

G. bulloides 10 32 22 9 69 28 −0.86 0.02

G. glutinata 11 25 13 8 52 32 −0.93 −0.01

T. quinqueloba 12 21 15 5 71 24 −0.94 0.01

N. incompta 13 22 12 5 55 23 −0.76 0.01

N. pachyderma 14 16 11 4 69 25 −0.91 0.00

G. scitula 15 18 10 2 56 11 −0.30 0.02

G. calida 16 9 4 3 44 33 −0.82 0.02

G. falconensis 17 6 4 2 67 33 0.14 0.01

G. theyeri∗ – 5 2 2 40 40 – –

G. uvula∗ – 5 4 0 80 0 – –

Group C

G. inflata 18 13 10 1 77 8 0.15 0.08

G. truncatulinoides 19 12 9 0 75 0 −0.02 0.09

Group average

A 51 29 −0.40 −0.14

B 60 26 −0.67 0.01

C 76 4 0.07 0.08

0 90 180 270 360day of year

-2

-1

0

1

2

3

4

shel

l flu

x [lo

g 10(

# m

-2 d

ay-1)]

r2 = 0.84, p = 2.8 x 10-23

B

0 90 180 270 360day of year

0

1000

1000

1000

1000

1000

A

shel

l flu

x [#

m-2 d

ay-1] 2003

2004

2005

2006

2007

Figure 3. Example of the periodic regression approach. (a) Raw data plotted vs. year day and at middates of collection intervals. (b) Log-

transformed data and fitting approach; the thick red line represents the best fit for the two models tested. The dashed horizontal red line is the

periodic mean, the yellow arrow indicates the amplitude of the cycle and the green arrow indicates the modelled peak time. Data are from T.

quinqueloba from site 3.

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L. Jonkers and M. Kucera: Global analysis of seasonality in the shell flux 2213

ity in peak timing is similar to the precision of the periodic

regression puts important constraints on the method, imply-

ing that interannual variability in the peak timing (within ap-

proximately 1 month) cannot be resolved.

Considering that the average resolution of the trap series

is 19 days, interannual variability in peak timing of the same

type as observed in the Gulf of Lions series could only be

detected in less than half of the series covering more than 1

year. This would limit the analysis at the cost of generalisa-

tion. We thus decided to ignore the interannual component of

the variability and use annual composites of the entire time

series for further analyses. This ensures that all data sets, ir-

respective of their duration and resolution, are treated uni-

formly. In multiyear series, such analysis also alleviates the

problem of uneven sampling and allows the use of all data

despite sampling gaps.

The sinusoidal representation of the seasonal shell flux

pattern imposed by the periodic regression describes the vari-

ance in the annual composite data to varying degrees (see

Fig. S2 for all fitting results). The maximum adjusted coeffi-

cient of determination for the periodic regression is 0.88 (G.

bulloides at site 3), and the minimum coefficient still consid-

ered significant is 0.03 (Globigerinita uvula at site 33). On

average, the periodic regression model explains more than

one third of the variance, confirming that most of the flux

series have a substantial periodic seasonal component. Irre-

spective of the regression coefficient, the difference between

observed and estimated peak timing is small (mean error:

−7 days; Figs. 2 and S3 for individual species), with most

of the differences being within the average resolution of the

time series. Nevertheless, there seems to be a tendency by

the periodic regression to overestimate the timing of the peak

(Fig. 2). Although the amount is below the resolution of most

time series, this small bias may reflect an asymmetry in the

peak flux pattern with higher fluxes following but not preced-

ing the peak.

With respect to the amplitude of the seasonal signal, the

amplitude estimated by the periodic regression is always

smaller than the observed amplitude (Fig. S2), which sim-

ply reflects the nature of the least-squares regression. In fact,

even the raw data are unlikely to reflect the actual ampli-

tude of the seasonal flux because of the time averaging of

the sampling. Given the different time resolution and gaps,

the estimate based on periodic regression is likely to be more

robust. Within the limitations described above, the general

performance of the periodic regression is satisfactory and al-

lows for the objective characterisation of the seasonal shell

flux patterns.

3.2 Seasonality in shell flux

The periodic regression analysis indicates the presence of a

statistically significant cyclic component in more than 80 %

of the annual composite flux series (Table 2, Fig. S2). Of

those cases, the majority is characterised by a single yearly

flux peak. Only a single site (no. 30, off Brazil) completely

lacks predictable seasonality. Other than a weak tendency

for a smaller incidence of seasonality in the tropics, there

is no strong pattern in the prevalence of seasonality with lat-

itude or with annual temperature (Fig. 4a, b). The propor-

tion of seasonal patterns characterised by two cycles per year

seems to be higher at the extremes of the temperature range

(Fig. 4a) and flux series with double cycles appear to be more

frequently found in the Pacific Ocean (Fig. S5). The sea-

sonal temperature range does not appear to show a relation-

ship with the occurrence of seasonality in the shell flux, nor

with the number of cycles characterising the seasonal pattern

(Fig. 4c).

The peak prominence in flux series with a significant sea-

sonal component varies between −2 and 2, indicating dif-

ferences in the peak flux over the mean flux by 4 orders of

magnitude. At low latitudes seasonal shell flux patterns are

characterised by generally low peak prominence, and the pro-

portion of patterns with intermediate peak prominence (−1

to 0) decreases with latitude (Fig. 4d). Figure 4e shows that

warm-water sites are indeed characterised by relatively low

peak prominence, but at low temperature, peak prominence

is more variable and shell flux patterns with even lower peak

prominence are observed in cold waters. The seasonal tem-

perature range seems not to be a predictor of peak promi-

nence (Fig. 4f).

A significant seasonal component was found in the flux

data for all 23 species analysed. In four species, signifi-

cant cyclicity was only observed at fewer than five sites (Ta-

ble 2). To avoid a bias due to low observation density, these

species were excluded from analyses of species-specific pat-

terns. Since temperature is known to be an important predic-

tor of zooplankton phenology (Mackas et al., 2012; Richard-

son, 2008), we initially compared the patterns of seasonality

(peak timing and peak prominence) to mean annual temper-

ature at each site averaged over the upper 50 m. By exam-

ining the relationship with temperature of the proportion of

annual flux patterns with two cycles per year, peak timing

and peak prominence, we were able to identify three groups

of species with distinct modes of seasonality (Fig. 5). A par-

ticularly strong discriminator seems to be the relationship be-

tween peak prominence and temperature (see PP’ in Fig. 5)

and the presence of a systematic shift in peak timing with

temperature. The behaviour of the species belonging to each

group seems independent of the ocean basin and the presence

or absence of upwelling (Figs. 5, S6).

3.2.1 Tropical and subtropical species (group A)

This group comprises Globigerinoides ruber (white and pink

varieties), Neogloboquadrina dutertrei, G. sacculifer, Or-

bulina universa, G. siphonifera, Pulleniatina obliquilocu-

lata, Globorotalia menardii, Globoturborotalita rubescens,

Globoturborotalita tenella and Globigerinoides conglobatus.

These species are characterised by a pattern best illustrated

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2214 L. Jonkers and M. Kucera: Global analysis of seasonality in the shell flux

0 10 20 30 40 50 60 70latitude

0

20

40

60

80

100

frequ

ency

[%]

29 51 33 86 69 39 9

0 5 10 15 20 25 30MAT [°C]

22 54 59 58 43 80

-2 2 3.5 5 6.5 8 9.5 11ATR [°C]

86 67 39 63 33 28

1 cycle yr-1 2 cycles yr-1 no fit

CBA

0 10 20 30 40 50 60 70latitude

0

20

40

60

80

100

frequ

ency

[%]

<-2 -2 to -1 -1 to 0 0 to 1 >1

2 5 10 15 20 25 30MAT [°C]

2 3.5 5 6.5 8 9.5 11ATR [°C]

FED

-

PP:

Figure 4. General patterns in shell fluxes. Top row: proportion of annual shell flux patterns showing seasonality and those characterised by

one or two cycles per year vs. absolute latitude (a), mean annual temperature (b) and annual temperature range (c). Numbers denote sample

size (n). Bottom row: peak prominence distribution vs. same parameters as above.

by G. ruber (white) and G. sacculifer, where peak timing

varies randomly in warm waters (> 25◦ C; Fig. 5a) and be-

comes more focussed in autumn in colder waters. Species

such as P. obliquiloculata and G. menardii that have a re-

stricted high-temperature range do not show this latter char-

acteristic (Fig. S6). The focussing of peak timing in autumn

in these species is associated with a clear increase in PP to-

wards colder waters (Fig. 5). In the group as a whole, the

temperature dependence of PP, expressed as the slope of a

linear regression between PP and temperature, is negatively

correlated with the mean temperature where the species is

observed in our compilation (Tspec) and positively with tem-

perature range (1Tspec) (r2= 0.6 and 0.9, respectively, ex-

cluding P. obliquiloculata; Fig. 6b, d). In addition, for a given

Tspec, these species show a low proportion of flux series with

two cycles per year (Table 2), and this proportion generally

increases with temperature (Figs. 6a, c and S7) such that

species observed in warmer waters (and observed within a

narrower temperature range) have proportionally more dou-

ble cycles (r2= 0.7; Fig. 6c).

3.2.2 Temperate and cold-water species (group B)

Globigerina bulloides, Globigerinita glutinata, T. quin-

queloba, N. incompta, N. pachyderma, Globorotalia scitula,

Globigerinella calida, Globigerina falconensis, Globoro-

talia theyeri and G. uvula form the second group. These

species show a flux pattern best represented by G. glutinata

(Fig. 5), with two possible peak timings per year, both shift-

ing towards later in the year at lower temperatures (Fig. 5b).

In contrast to group A, the proportion of records with dou-

ble cycles seems to decrease with temperature in this group

(Fig. S7). In some cases a third, intermediate peak time,

showing a similar trend with temperature, is visible (G. sc-

itula, G. bulloides), and in other cases (e.g. N. pachyderma)

the trends are not as clear (Fig. S5). None of these species

shows a clear trend in peak prominence with temperature

(PP’∼ 0; Fig. 6; Table 2). With the exception of G. scitula

and G. falconensis, the average PP of species in the group

lies within a narrow range around −0.9 (Table 2) and is not

related to temperature across the species in the group (Fig. 6).

For any given Tspec, the species show a higher proportion

of series with two cycles per year than in group A (Fig. 6),

but similarly to group A, the proportion of double cycles in-

creases with Tspec and 1Tspec (r2= 0.7 and 0.6 respectively,

excluding G. scitula; Fig. 6a, c).

3.2.3 Deep dwellers (group C)

This group consists of two species: G. inflata and G. truncat-

ulinoides. Compared to the other groups, the proportion of

flux series that showed only a single cycle per year is high-

est; only a single time series with two cycles per year is ob-

served (Table 2). The peak timing in both species is inde-

pendent of temperature and occurs everywhere in winter and

early spring (Fig. 5c). Peak prominence is high (mean 0.07),

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L. Jonkers and M. Kucera: Global analysis of seasonality in the shell flux 2215

0 10 20 30

0

100

200

300pe

ak ti

me

[DO

Y]

G. ruber (white)

0 10 20 30T0-50m [°C]

-2

-1

0

1

PP

0 10 20 30

0

100

200

300

G. glutinata

0 10 20 30T0-50m [°C]

-3

-2

-1

0

1

2

Atlantic Indian Pacific

0 10 20 30

0

100

200

300

0 10 20 30T0-50m [°C]

-2

-1

0

1

2

G. truncatulinoides

open symbols denote upwelling

Group A Group B Group C

Figure 5. Three modes of seasonality: examples of each group; for all species see Fig. S6. Peak timing (day of year (DOY) on NH scale)

and peak prominence (PP) vs. mean annual temperature between 0 and 50 m. Group A: warm-water, symbiont-bearing species. Group B:

temperate and cold-water species. Group C: deep dwellers. The grey solid curves in A illustrate the peak timing and prominence and the

dashed grey line highlights the linear relation between PP and temperature (PP’). Note that peak timing represents circular data and that for

G. truncatulinoides peak timing close to DOY 365 has been plotted as a negative value to highlight the pattern.

and these species are the only ones indicating a positive PP’

(Figs. 5c, 6, Table 2).

4 Discussion

Our global compilation of shell flux time series from sed-

iment traps shows that spatially variable seasonality is

a ubiquitous and widespread phenomenon in planktonic

Foraminifera. Seasonality characterises the majority of the

time series investigated at any latitude or temperature

(range). The amplitude of the seasonal cycle and hence the

effect of seasonality on the sedimentary record varies, how-

ever, among species and with temperature. According to

their seasonality pattern, the studied species can be classi-

fied into three groups. Importantly, this division holds for ev-

ery ocean basin and is not affected by upwelling. This sug-

gests that these groups reflect three principal modes of sea-

sonality. Tropical and subtropical, mostly symbiont-bearing

species (group A) adjust the peakedness of their seasonal

flux pattern. Temperate and cold-water species (group B)

show the opposite picture and only shift the timing of peak

flux during the year without changing the peak prominence.

Deep dwellers have approximately constant peak timing and

change the shape of the flux pattern, but in the opposite di-

rection from group A. Indeed, the direction of the PP change

with temperature or sign of PP sensitivity (sign of PP’) is the

most robust discriminator between the three groups (Fig. 6).

Although the analysis revealed a high prevalence of a pre-

dictable component in the seasonal flux patterns, the periodic

sinusoidal models used to detect and characterise seasonality

only explained, on average, one third of the variability in the

data. This may suggest that a significant part of the observed

variability in the flux is not predictable. The additional vari-

ability may reflect small differences in flux timing among

years, high-frequency variability such as lunar cycles, long-

term trends, non-periodic events or random noise. In addi-

tion, the scatter in the data may, at least in part, also be due

to the nature of the flux observations itself.

Several studies have shown that sediment traps moored at

the same position but at different depths may record differ-

ent shell fluxes (e.g. Curry et al., 1992; Mohiuddin et al.,

2004). Such differences could be due to dissolution in the

water column (Thunell et al., 1983), (lateral) advection of

shells from different areas (Von Gyldenfeldt et al., 2000) or

temporal lags reflecting the settling time of the shells (Taka-

hashi and Bé, 1984). We have tried to always use the up-

permost trap in order to minimise these effects, but it is im-

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2216 L. Jonkers and M. Kucera: Global analysis of seasonality in the shell flux

4 8 12 16 20 24 28Tspec [°C]

0

10

20

30

40

50

2 cy

cles

yea

r-1 [%

]

1 69

2

7

5

8

3

4

10

11 71 61

15

13

14 12

19

18

4 8 12 16 20 24 28Tspec [°C]

-0.6

-0.4

-0.2

0

0.2

PP' [°C

-1]

16

92

7

5

8

34

10

11 161715

131412

1918

ABC

0 10 20 30ΔTspec [°C]

0

10

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2 cy

cles

yea

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]

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101116 17

15

13

1412

19

18

0 10 20 30ΔTspec [°C]

-0.6

-0.4

-0.2

0

0.2

PP' [°C

-1]

16

9

2

7

5

8

3 4

10

11

16 17 15

13

1412

1918

A B

C D

Figure 6. Scatter plots of the proportion of time series with two cycles per year (a) and temperature sensitivity of peak prominence (PP’)

(b) vs. temperature averaged over all sites where a species was observed (Tspec). Panels (c) and (d) are arranged in the same way as (a) and

(b) , but vs. temperature range (1Tspec). Individual species are numbered (see Table 2).

possible to exclude the possibility that part of the observed

noise in the timing and strength of species flux is due to pro-

cesses that have modified the primary export flux. However,

the magnitude of this error is probably within the same range

as for interannual variability and therefore within the range

of error of the periodic regression approach. For instance, for

settling speeds, studies have shown that, depending on shell

weight, size and shape, sinking speeds can vary over 2 orders

of magnitude (Takahashi and Bé, 1984; Von Gyldenfeldt et

al., 2000). For the deep traps, settling times could thus be

as little as 4 days or as much as 1 month. This would intro-

duce a unidirectional temporal offset from the primary sig-

nal near the ocean surface and the delivery of the flux to the

sediment trap, the size of which would vary with trap depth

and shell size. However, the temporal offset thus caused is

within the uncertainty caused by applying the periodic re-

gression (Fig. 2). Similarly, large seasonal changes in the

catchment area and differential dissolution during settling

would have most affected the deepest traps. However, be-

cause of our preference for shallower traps (only two of the

analysed records are from traps moored deeper than 4000 m

and only 11 of the 37 traps were moored deeper than 3000 m;

Table 1), this effect is probably small.

Moreover, small but consistent differences in the season-

ality of shell flux in different size classes have been reported

(Jonkers et al., 2013; Thunell and Reynolds, 1984). The data

presented here are mostly from the more than 125 or 150 µm

fraction, and the fraction analysed was always held constant

within each trap series. As a result, the effect of size dif-

ferences on the observed phenology is probably limited. In

contrast, the magnitude of the shell flux is more size depen-

dent. Since small shells tend to be more abundant (Berger,

1969), the inclusion of smaller-sized shells could cause large

changes in the amplitude of flux variation. However, includ-

ing smaller specimens would also increase the background

flux, and by normalising the amplitude to the mean flux (peak

prominence), we have largely excluded the size effect when

comparing data from different records.

Next, this compilation includes assemblage counts car-

ried out by many different researchers. Consequently, com-

plete taxonomic consistency between all the different stud-

ies cannot be guaranteed. This may in particular hold for

the neogloboquadrinids in the Pacific, where different coil-

ing directions are distinguished in both N. incompta and N.

pachyderma (Kuroyanagi et al., 2002). Furthermore, analyt-

ical errors resulting from counting and splitting have, to our

knowledge, never been properly quantified but are probably

minimal compared to the size of the seasonal signal and are

almost certainly random.

Even if one assumed that the flux time series are a robust

representation of the flux pattern of each species as it oc-

curred in the year of collection, the interpretation of the ob-

served patterns could be obscured by the existence of genetic

diversity within species. The analysed shell flux data are

Biogeosciences, 12, 2207–2226, 2015 www.biogeosciences.net/12/2207/2015/

L. Jonkers and M. Kucera: Global analysis of seasonality in the shell flux 2217

based on morphospecies, and many of those include multi-

ple distinct genetic lineages (“cryptic species”), which might

have distinct ecological habitats and may be endemic to sep-

arate ocean basins or regions (e.g. Darling et al., 2007). If

the phenology of the constituent cryptic species were dif-

ferent, the patterns based on morphological species may be

spurious. However, the observed modes of seasonality appear

to be independent of ocean basins (Figs. 5, S6) and display

consistent features among different species. This suggests

that the seasonality of cryptic species is similar within one

morphospecies. Apparently, ecophysiological differences be-

tween morphospecies could be responsible for some noise in

the data, but they are too small to change the attribution to

one of the three groups. This is consistent with the three main

modes of seasonality occurring across different taxa, reflect-

ing a functional aspect of species ecology and their environ-

ment rather than their taxonomic attribution.

4.1 Environmental controls on shell flux seasonality

Temperature exerts a major influence on the spatial distribu-

tion of planktonic Foraminifera species (Bé and Tolderlund,

1971; Morey et al., 2005), and it is likely that the temporal

distribution (seasonality) is likewise influenced by temper-

ature. Indeed, in other groups of zooplankton, temperature

appears to be the best predictor of phenology (e.g. Mackas et

al., 2012; Richardson, 2008), although the exact mechanism

by which temperature controls phenology is not fully under-

stood (Mackas et al., 2012). The three different modes of sea-

sonality that we identified in planktonic Foraminifera also in-

dicate that different species respond differently to changing

environmental parameters, and a potential temperature effect

is complex.

Temperature can affect zooplankton phenology in different

ways (Mackas et al., 2012). It could directly affect phenol-

ogy through the acceleration of metabolic processes. Such

a direct physiological effect may be what drives, or con-

tributes to, the earlier-when-warmer pattern in the species of

group B. However, in some groups of zooplankton, the ob-

served changes in seasonality are larger than expected from

the metabolic effect of temperature alone, rendering such a

direct temperature control unlikely (Mackas et al., 2012).

Moreover, shell growth and survival appear to reach a satu-

ration level at given temperatures in planktonic Foraminifera

(Bijma et al., 1990); above this level temperature does not

have any direct effect on flux. Consequently, a direct temper-

ature effect is unlikely to fully explain the observed seasonal

behaviour amongst the different species.

An alternative explanation for the effect of temperature on

seasonality is that individual species have a thermal niche:

a preferred temperature range with an optimal balance be-

tween growth and respiration. Reproductive success would

then simply follow temperature, leading to the observed sea-

sonality in the shell fluxes (e.g. Lombard et al., 2011; Zaric

et al., 2005). This explanation also takes into account the dif-

ferences in the temperature zonation among species (Bé and

Tolderlund, 1971) and is confirmed by observations of a rela-

tionship between highest abundance and largest sizes (Hecht,

1976). It is therefore probably a better mechanism to explain

the role of temperature than a direct metabolic control would

be.

Finally, temperature may simply serve as a predictor or

cue of optimal conditions (Mackas et al., 2012). This would

imply that species use (changes in) temperature as an indica-

tion of favourable conditions to time their reproduction rather

than there being any (in)direct advantages associated with a

certain temperature range. Such a “signalling” role of tem-

perature is important for reproduction timing in organisms

that live in seasonally variable environments and that are de-

pendent on correctly anticipating the arrival of optimum con-

ditions for their offspring.

It is important to note, however, that, in large parts of the

ocean, temperature is correlated to a number of other param-

eters, including factors that control phytoplankton produc-

tivity. Consequently, it remains difficult, in part due to the

lack of data on these correlates, to assess whether temper-

ature really is the most important factor in determining the

seasonality in planktonic Foraminifera (or in zooplankton in

general) or whether the correlation between temperature and

seasonality is a statistical artefact.

4.1.1 Warm-water species

With the exception of G. siphonifera and O. universa, species

in this group have relatively narrow and warm thermal

optima above 20◦ C (Bé and Tolderlund, 1971; Zaric et

al., 2005). At high temperatures the flux is relatively even

throughout the year (low PP), and peak timing appears to

be distributed throughout the year (Figs. 5, S6). Neverthe-

less, even at the warmest sites, the periodic regression indi-

cates a significant seasonal component, often with two cy-

cles per year, in the flux series of these species. Despite the

low amplitude of the observed flux seasonality, the periodic

model often explains a large portion of the variance in the

data (e.g. 40 % for G. ruber (white) at site 31 in the Mozam-

bique Channel or over 50 % for several species at site 27 in

the South China Sea). However, we note that almost all of

the tropical sites are affected by local seasonal change in en-

vironmental conditions, in each case reflecting different pro-

cesses, such as the monsoon in the South China Sea and the

Arabian Sea and upwelling in Cariaco Basin and off Java. It

seems that these processes are sufficiently strong to pace the

flux of planktonic Foraminifera species at most locations in

the tropics, but the resulting seasonal pattern has a relatively

low amplitude.

With decreasing temperature, the flux pattern of these

warm-water species becomes more predictable: the peak

prominence increases and peak timing is progressively fo-

cussed towards the autumn (Figs. 5, S6). This behaviour

can be explained by the existence of a species-specific ther-

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2218 L. Jonkers and M. Kucera: Global analysis of seasonality in the shell flux

mal optimum. In tropical areas, optimum temperatures occur

year-round, whereas outside the tropics, they occur only for a

short duration during the warm season. Consequently, these

species concentrate a larger proportion of the annual flux in

a shorter period (increasing PP at lower mean annual tem-

peratures). Species showing a more restricted tropical pref-

erence show a higher PP’, indicating that the tendency to

concentrate the flux is greater in species with a narrow high-

temperature habitat (Fig. 6). Interestingly, with decreasing

temperature, only N. dutertrei shifts its maximum flux to the

period of maximum temperature, whereas the other species

seem to time the flux peak to 1–2 months after the tempera-

ture peak (Figs. 7, S8). This may reflect different growth rates

among these species, with N. dutertrei having a higher fecun-

dity or a shorter generation time. The cessation of the peak

flux would then represent a thermal barrier to reproduction

or growth. In this way, the observed pattern would represent

strong evidence for the existence of a thermal niche in these

species.

The presence of symbionts in this group is in line with

the dominant temperature control on the seasonal flux pat-

tern of these species. With the exception of G. rubescens,

which was never under systematic investigation, all other

species in this group have been found in association with al-

gal endosymbionts (Gastrich, 1987; Spindler and Hemleben,

1980), which decrease their reliance on primary productiv-

ity. To test this, we compared the timing of maxima in sur-

face chlorophyll concentrations with maxima in the shell flux

(Fig. 8). This analysis reveals a wide and relatively even dis-

tribution of temporal offsets between peak flux and peak pro-

ductivity, indicating no relationship between these variables.

Although the number of cases is relatively small, it is pos-

sible to evaluate the association between shell flux and pro-

ductivity quantitatively. To this end, we consider a peak in the

flux as coinciding with a chlorophyll maximum as long as the

peak flux occurs within 49 days before to 79 days after the

chlorophyll maximum. This interval is chosen to account for

the uncertainty due to discrete observations of shell flux (av-

erage resolution of 19 days) and the chlorophyll data (resolu-

tion of 1 month), adding a plausible lag of 1 month between

productivity and the response of Foraminifera. This analy-

sis reveals a possible, statistically significant association be-

tween shell flux and productivity (confidence interval: 95 %;

binomial test) only for G. siphonifera and P. obliquilocu-

lata, where it is driven by observations from upwelling sites,

showing a lead of Foraminifera flux peaks over productivity

maxima that is within the uncertainty interval.

4.1.2 Temperate and cold-water species

Most species that are typically associated with temperate

to cold-water conditions show two possible flux peak tim-

ings per year and exhibit a tendency to shift their peak(s)

to earlier in the year at higher temperatures (Fig. 5b). Such

an earlier-when-warmer trend is widely observed in other

(zoo)plankton groups (Mackas et al., 2012). In this group,

temperature appears to affect the timing of both flux peaks,

and the earlier-when-warmer pattern suggests a temperature

control on the peak timing in this group. However, a direct

metabolic control on peak timing is unlikely because both

seasonal flux peaks in these species do not coincide with tem-

perature maxima. Alternatively, if the species would have a

clearly defined thermal niche, it would be expected that peak

fluxes occur everywhere approximately within this tempera-

ture range (but at different times in the year). However, peak

fluxes in the temperate to cold-water species occur over a

wide temperature range (> 20◦ C for some species, e.g. G.

bulloides and G. glutinata; see Figs. 7, S8). This indicates

that the existence of species-specific thermal niches is un-

likely to explain the earlier-when-warmer patterns. In fact,

this is in agreement with the wide temperature tolerance for

most of these species (Tolderlund and Bé, 1971; Zaric et al.,

2005). Consequently, we entertain the possibility that the re-

lationship between temperature and peak timing could stem

from a correlation between temperature and the actual deter-

minant of shell flux.

The presence of two cycles (blooms) per year, one in

spring and a second after the breakdown of the summer strat-

ification, is also a common feature of phytoplankton blooms

at midlatitudes (e.g. Edwards and Richardson, 2004). The

timing of these blooms is to some degree dependent on tem-

perature, and an association of peak flux in the species of this

group with productivity is thus a plausible candidate mech-

anism. A reliance on productivity in this group is also con-

sistent with the lack of symbionts in most of its species. The

presence of symbionts has been suggested for G. falconensis,

G. glutinata and T. quinqueloba. However, Gastrich (1987)

did not observe algal symbionts in G. falconensis, and the

suggestion of symbionts in this species, mentioned in Spero

and Parker (1985), derives from a misidentification of this

species (Spero, personal communication, 2014). In addition,

the presence of symbionts in T. quinqueloba is mentioned in

the description of this species in Hemleben et al. (1989), but

no information is provided on the origin of this observation.

Only in G. glutinata is an association with symbionts sup-

ported by direct observations (Gastrich, 1987).

Having identified productivity as a potential factor control-

ling the flux phenology in temperate to cold-water planktonic

Foraminifera species, we compared the timing of maxima in

surface chlorophyll concentration with maxima in the shell

flux, following the same approach as for the warm-water

species (Fig. 8). Indeed, most species in this group show an

association between the timing of peak flux and chlorophyll

maxima. Only in G. calida and G. scitula is the association

not statistically significant, and G. falconensis has too small

a number of observations. The dominance of a significant re-

lationship between the timing of shell flux and chlorophyll

maxima supports previous studies that indicated a link be-

tween productivity and shell flux in the temperate species

(Hemleben et al., 1989; Schiebel et al., 1997). Thus, our anal-

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L. Jonkers and M. Kucera: Global analysis of seasonality in the shell flux 2219

0 5 10 15 20 25

010

020

030

0

G. ruber (white)

MAT [℃]

DOY

●

●

●

0 5 10 15 20 25

G. glutinata

MAT [℃]

●

●

0 5 10 15 20 25

G. truncatulinoides

MAT [℃]

0510152025

Group A Group B Group C

Figure 7. As Fig. 5, but peak timing patterns plotted over contour plots of annual temperature cycle. See Fig. S8 for other species. Symbols

denote ocean basins: squares represent the Atlantic Ocean, circles the Indian Ocean and triangles the Pacific Ocean. Open symbols indicate

sites affected by upwelling.

ysis suggests that the timing of primary productivity serves

as a predictor of peak flux timing in the temperate to cold-

water species.

4.1.3 Deep-dwelling species

Of the three deep-dwelling species analysed, two appear

to show a unique pattern of seasonality, distinct from both

groups described above. Whereas G. scitula shows a pattern

consistent with group B, the peak flux of G. truncatulinoides

and G. inflata is observed around the same time in winter

or spring (Fig. 5c) and a large proportion of the annual flux

occurs in a single high-flux pulse (high PP, low percentage

of double cycles; Table 2). Both characteristics might relate

to a longer life cycle, as proposed for G. truncatulinoides

(Hemleben et al., 1989). The two species show a (weak)

positive correlation between peak prominence and temper-

ature (Fig. 5c), which could indicate higher year-round re-

productive success at lower temperatures, in agreement with

the preferred peak timing around the coldest time of the

year and also with their preferred temperature range (Bé and

Tolderlund, 1971; Tolderlund and Bé, 1971). Alternatively,

the more evenly distributed seasonal flux pattern outside the

tropics could reflect a higher proportion of expatriated spec-

imens that have higher mortality outside the peak flux pe-

riod, but this does not agree with the higher abundance of

both species at lower temperatures (Bé and Tolderlund, 1971;

Tolderlund and Bé, 1971). As expected, because of the rel-

atively constant timing of peak flux in both species, there is

no association between the timing of their flux and that of

productivity.

The peak shell flux around the coldest month during the

year (clearest in G. truncatulinoides) argues against a direct

metabolic influence of temperature on the peak timing. Sim-

ilarly, if these species had a well-defined thermal niche be-

tween ∼ 10 and 24◦ C, as suggested by Bé and Tolderlund

(1971), one would expect that, at both ends of that tempera-

ture range, the peak flux would represent a larger proportion

of the total annual flux because the seasonal growth window

would have become shorter. However, we do not observe

such an increase in the PP at the cold end of the temper-

ature range (Fig. 5c). This argues against temperature con-

trolling the seasonal pattern and suggests that either some

correlated factors determine the seasonality in this group or

that the species use some feature of the seasonal temperature

cycle as a cue to anticipate optimal conditions for their off-

spring. Although both species are considered deep dwellers,

they migrate vertically through the water column and spend

a significant part of their life cycle relatively close to the

sea surface (Field, 2004; Loncaric et al., 2006; Tolderlund

and Bé, 1971). Thus, the trigger for the regular flux pulse

could originate from conditions close to the surface, where

seasonal environmental changes tend to be of a higher mag-

nitude.

4.2 Paleoceanographic implications

The existence of a significant seasonal component in the

shell flux pattern of extant planktonic Foraminifera has im-

plications for the interpretation of the fossil record. Since

seasonality is species-specific and spatially variable, fossil

assemblages of the same species collected at different lo-

cations contain a different amount of seasonal bias in the

composition of their shells. Firstly, this may affect proxy

calibrations based on sediment core tops, which, depend-

ing on their location, will reflect a variable amount of sea-

sonal bias and not reflect mean annual conditions. Because

of such seasonal bias, determining the environmental niche

of a certain species using mean annual conditions leads to

an overestimation of the width of this niche. Consequently,

under these assumptions, calibration based on mean annual

environmental conditions may not be meaningful. Hönisch

et al. (2013) provide an instructive example of how seasonal-

ity may affect Mg /Ca temperature calibration, but seasonal

biasing may also affect multivariate biotic approaches based

on foraminiferal assemblages.

Secondly, this spatial bias may translate into a temporal

bias in records straddling climatic transitions. The size of the

www.biogeosciences.net/12/2207/2015/ Biogeosciences, 12, 2207–2226, 2015

2220 L. Jonkers and M. Kucera: Global analysis of seasonality in the shell flux

048

121620

G. bulloides*

02468

10

G. glutinata*

02468

10

T. quinqueloba*

02468

10

N. incompta*

-147-98-490 49 98 147

offset [days]

012345

N. pachyderma*

-147-98-490 49 98 147

offset [days]

012345

G. scitula

-147-98-490 49 98 147

offset [days]

012345

G. calida

-147-98-490 49 98 147

offset [days]

012345

G. falconensis

012345

G. ruber (white)

012345

G. ruber (pink)

0

2

4

6

G. sacculifer

02468

G. siphonifera*

012345

O. universa

02468

P. obliquiloculata*

012345

G. rubescens

012345

G. menardii

012345

N. dutertrei

Group A

Group B

Figure 8. Productivity and peak timing in groups A and B. Histograms of the residual of chlorophyll maxima and peak flux timing. Zero and

positive offsets suggest an association between shell flux peaks and productivity. The interval used for the binomial test is highlighted by the

black horizontal bar, and species that show a statistically significant association (p≤ 0.05) between shell flux peak timing and productivity

are marked with an asterisk.

bias is a function of the amplitude of the seasonal cycle (PP),

the variance in the timing of peak flux and the interannual

amplitude of the change in the environmental parameter to

be reconstructed. Our compilation of shell flux time series

allows an assessment of the potential size of such a bias in

the fossil record by calculating a flux-weighted average tem-

perature for a synthetic sample that would be deposited be-

low the location of each trap. Figure 9 shows the spatial dis-

tribution of the seasonal bias within each species expressed

as a difference between the flux-weighted temperature and

the mean annual temperature (0–50 m water depth). Species-

specific depth habitats were not taken into account in the cal-

culation of these offsets, which likely leads to an overestima-

tion of the size of the bias for deep-dwelling species. If the

flux of a species is even throughout the year or the tempera-

ture does not vary, the offset would tend to zero. If the flux

shows peaks of similar size and constant timing, the offsets

for one species will always have the same sign. In all other

cases the distribution of the bias will be more complex, pre-

cluding generalisations, such as a classification into summer

or winter species.

The temperature offsets due to seasonality vary between

+4 and −4◦ C and may range by as much as 6◦ C within

one species (Fig. 9). At a single location, species with a dif-

Biogeosciences, 12, 2207–2226, 2015 www.biogeosciences.net/12/2207/2015/

L. Jonkers and M. Kucera: Global analysis of seasonality in the shell flux 2221

G. ruber (white)

●●●●● ●●● ●● ●●●● ●● ●● ●

●

N. dutertrei

●●●●●● ●●●●●● ●

●●

●●

G. sacculifer

● ●●●●●● ●● ●●● ●● ●● ●●

O. universa

● ●● ●● ●●●● ●●● ●●

●

●● ●

G. siphonifera

●● ●●●●●●● ●● ●●●

● ●●

G. ruber (pink)

●●● ●●

●●

P. obliquiloculata

●●●●●● ●

●

G. menardii

●●●● ●

●

G. rubescens

●●●●●

●

G. bulloides

●●●● ●●● ● ●●● ●●●●● ●●●● ● ●●●● ●

●● ●●

● ●

G. glutinata

●●● ●●● ● ●●●●●●●●● ●

●●●● ●●

●● ●

T. quinqueloba

●●●● ●●● ● ●●●●●● ●

●●●

●● ●

N. incompta

●●● ●● ● ●●● ●●●● ●● ●●

●●

●● ●

●

N. pachyderma

●●●● ●●● ●●●

●

●● ●

● ●

G. scitula

● ●●● ● ●●●●●●● ●

●●

●● ●

G. calida

●●●●●●

●

●●

G. falconensis

●●●

●

● ●

G. truncatulinoides

●● ●●●●●●● ●

● ●

G. inflata

● ●● ●●●●●● ●

●● ●

●●●●●●●

[−4,−2.5][−2.5,−1.5][−1.5,−0.5][−0.5,0.5][0.5,1.5][1.5,2.5][2.5,4]

temperature offsetfrom annual mean

Figure 9. Offset from mean temperature (0–50 m water depth) due to seasonality. Positive offsets indicate that flux-weighted temperature is

higher than mean temperature and vice versa.

www.biogeosciences.net/12/2207/2015/ Biogeosciences, 12, 2207–2226, 2015

2222 L. Jonkers and M. Kucera: Global analysis of seasonality in the shell flux

ferent seasonality mode may show different flux-weighted

offsets. This is more likely to occur at intermediate tem-

peratures where warm and cold-water species mix and has

been observed previously (Jonkers et al., 2013; King and

Howard, 2005). Large positive offsets are generally found at

higher latitudes, reflecting a preference for summer flux in

colder regions. As a result of the earlier-when-warmer trend

in peak timing, negative offsets are observed in temperate

zones in many species of group B. The offsets are often larger

than the uncertainty of Mg/Ca temperature estimates (about

1◦ C; Anand et al., 2003) and translate to an approximately

2 ‰ range in δ18O. This means the present-day spatial pat-

tern in temperature offsets due to varying flux seasonality

within a species could be large enough to explain the magni-

tude of glacial–interglacial change in most parts of the ocean

(MARGO project, 2009). Indeed, seasonality has been in-

voked as a possible explanation of the observed discrepancy

between Mg/Ca paleotemperatures, other paleothermometers

and climate models (Laepple and Huybers, 2013; Leduc et

al., 2010; Lohmann et al., 2013).

In addition, changes in the shape of the seasonal flux pat-

tern, particularly in the peak prominence, could influence

the variability in proxy records. A seasonal flux pattern with

higher PP means that the Foraminifera register ambient sea-

water conditions during a shorter interval in the year and this

will result in a narrower distribution of individual shell sig-

natures in the sediment. This not only reduces intra-sample

variability but, since only a small fraction of the total num-

ber of Foraminifera is used for analysis, also the variability

between samples (Laepple and Huybers, 2013).

Both effects clearly illustrate the importance of seasonal

variation in shell fluxes on the fossil record and the need to

take potential transient changes in seasonality into account.

Importantly, a transient change in the seasonal bias as a result

of changing environmental factors will depend on the mode

of seasonality that characterises the species. Thus, although

the magnitude of the temperature offsets due to seasonality

is large within species (Fig. 9), in reality, the presence of

the distinct seasonality modes means that the expected bias

through time due to shifting seasonality varies predictably in

its sign and magnitude.

Within the optimal temperature range of warm-water

species (group A), seasonal variation in the shell flux is low,

and these species thus record average annual conditions with

very little offset (Fig. 9). However, outside their optimum

temperature range, these species concentrate a progressively

larger portion of their annual flux into a single peak in au-

tumn, leading to an increasingly positive bias from mean an-

nual conditions. Consequently, during a cooling trend, the

seasonality of these species would shift towards the summer

and the proxies based on such species would underestimate

the magnitude of the temperature shift. Conversely, during

a warming trend, these species will shift their seasonality

from dominantly autumn to a more even flux throughout the

year, also leading to an underestimation of the actual change

in mean temperature. The resulting intra-sample variability

due to seasonality will remain similar, so single-shell anal-

yses are unlikely to resolve this effect. Because of the ob-

served pattern of shifting peak timing at present, temperate

and cold-water species (group B) are most likely to exhibit

transient changes in seasonality. As is the case in group A, a

shift in seasonality during climate transitions will lead to an

underestimation of the actual environmental changes recon-

structed using these species. During a warming trend, these

species will shift the peak timing towards earlier in the year,

recording lower temperatures and vice versa. Finally, the fos-

sil record of deep-dwelling species (group C) predominantly

reflects late winter or spring conditions. However, as the peak

prominence in this group increases with temperature, this

seasonal bias is larger at higher temperatures. Thus, during

a warming trend, more of the flux is focussed in the narrow

seasonal peak and the species records lower temperature, un-

derestimating the amplitude of temperature change. During

a cooling trend, the flux is less focussed, recording on aver-

age warmer conditions. Thus, remarkably, despite the three

principle modes of seasonality, transient changes in season-

ality always result in an underestimation of the amplitude of

environmental change recorded in fossil Foraminifera.

The recognition of the existence of distinct modes of sea-

sonality in planktonic Foraminifera provides an important

perspective for the validation of planktonic Foraminifera

growth models (Fraile et al., 2009a; Lombard et al., 2011;

Zaric et al., 2006). Rather than validating models by evaluat-

ing their performance at individual sites, it should be possible

to diagnose whether the model representation of individual

species is consistent with their observed seasonality mode.

Alternatively, the observed seasonality patterns can be used

to guide model development. For example, the strong rela-

tionship between PP and mean annual temperature in group

A, combined with the tendency to shift peak time towards

autumn, should be relatively easy to parametrise and imple-

ment, potentially allowing a more powerful prediction of the

effect of seasonality on the fossil record of species in this

group. Similarly, in group B, peak timing shows a strong as-

sociation with productivity. Primary productivity is explicitly

simulated in some models (e.g. Henson et al., 2009), offering

the chance to predict changes in seasonality of species in this

group from the seasonality of primary production. A com-

bination of models with a realistic representation of species

seasonality with proxy data certainly holds the best poten-

tial to estimate the actual amplitude of past environmental

change recorded in shells of planktonic Foraminifera.

5 Conclusions

Our systematic overview revealed that all 23 species of

planktonic Foraminifera studied have a predictable cyclic

component in the intra-annual variability in their shell

fluxes. This seasonality is spatially variable within individ-

Biogeosciences, 12, 2207–2226, 2015 www.biogeosciences.net/12/2207/2015/

L. Jonkers and M. Kucera: Global analysis of seasonality in the shell flux 2223

ual species and, depending on the species, predominantly

driven by temperature or by primary productivity. We identi-

fied three groups of species with similar modes of seasonal-

ity:

1. Tropical and subtropical species (G. ruber (white and

pink varieties), N. dutertrei, G. sacculifer, O. uni-

versa, G. siphonifera, P. obliquiloculata, G. menardii,

G. rubescens, G. tenella and G. conglobatus) have rel-

atively even flux patterns, with low prominence peaks

occurring at any time of the year in their optimum

high-temperature range (> 25◦ C). In colder waters peak

fluxes become more prominent and focussed in autumn.

This tendency is stronger in species with a narrow ther-

mal range. The prevalence of endosymbionts in these

species probably reduces their reliance on primary pro-

duction for food. Indeed, seasonality in this group ap-

pears to be primarily driven by temperature, suggesting

the existence of a thermal niche for these species.

2. Temperate and cold-water species (Globigerina bul-

loides, G. glutinata, T. quinqueloba, N. incompta, N.

pachyderma, G. scitula, G. calida, G. falconensis, G.

theyeri and G. uvula) show one or two cycles per year

and peak flux timing occurs later in the year at lower

temperatures. Peak timing in this group occurs at the

same time or slightly after maximum chlorophyll con-

centration, suggesting a tight link between primary pro-

ductivity and shell flux seasonality in these dominantly

symbiont-barren species.

3. Deep-dwelling species (G. inflata and G. truncatuli-

noides) always reach peak flux in winter or spring in-

dependently of temperature and productivity, but they

concentrate a larger proportion of the annual flux into

short periods in warmer waters.

The environmental control on shell flux seasonality im-

plies that changes in seasonality are likely to have occurred

during past climate change, leading to a change in the sea-

sonal bias in foraminiferal proxy records through time. Our

analysis indicates that the effect of this changing seasonal

bias leads to an underestimation of the change in mean con-

ditions, irrespective of the seasonality mode. Moreover, the

recognition of the three principal modes in seasonality of-

fers opportunities to test and improve foraminiferal growth

models, ultimately allowing for improved reconstructions of

climate change.

The Supplement related to this article is available online

at doi:10.5194/bg-12-2207-2015-supplement.

Acknowledgements. We thank all researchers who shared their

sediment trap time series data and William Howard and an anony-

mous reviewer for their positive reviews of a previous version of

this paper. This work was carried out within the framework of the

SCOR/IGBP working group 138: Modern Planktonic Foraminifera

and Ocean Changes. We thank Caroline Cléroux for her help in

initiating this project. L. Jonkers is funded through the Climate

Change Consortium of Wales (C3Wales.org).

Edited by: H. Kitazato

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